| Prof. Daniel J. Inman
Harm Buning Collegiate Professor and former Chair |
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Biography: Daniel J. Inman received his Ph.D. from Michigan State University in Mechanical Engineering in 1980 and is the Harm Buning Collegiate Professor and former Chair of the Department of Aerospace Engineering at the University of Michigan. Since 1980, he has published eight books (on vibration, energy harvesting, control, statics, and dynamics), eight software manuals, 20 book chapters, over 410 journal papers and 674 proceedings papers, given 78 keynote or plenary lectures, graduated 69 Ph.D. students, and supervised more than 75 MS degrees. He works in the areas of applying smart materials and structures to solve aerospace engineering problems including energy harvesting, structural health monitoring, vibration suppression and morphing aircraft. He is a Fellow of the American Institute of Aeronautics and Astronautics, American Society of Mechanical Engineers, International Instituted for Acoustics and Vibrations, Society of Experimental Mechanics and American Academy of Mechanics. He won the ASME Adaptive Structures Award in April 2000, SPIE Smart Structures and Materials Lifetime Achievement Award in March of 2003, he received the ASME Den Hartog Award for lifetime achievement in teaching and research in vibration, the 2009 Lifetime Achievement award in Structural Health Monitoring, and the AIAA Structures, Structural Dynamics, and Materials Award, in 2014. He is currently Technical Editor of the Journal of Intelligent Material Systems and Structures (1999-present).
Topic: Bioinspired Morphing using Smart Materials and Brain Inspired Computing
Abstract: Nature through careful observation and tests of gliding avian species have resulted in new thoughts on how to design morphing uninhabited air vehicles (UAV) and what morphing motions might make for better performance. An understanding of avian flight stability suggests a new approach to morphing aircraft design. Of interest is how to create these motions using smart materials to gain avian abilities. Coupled with new learning algorithms, methods for designing smart autonomous morphing airfoils for use in small UAVs are presented. Hardware based reinforcement learning (RL) techniques are used to teach a smart morphing wing to respond to gusts, following the inspiration of gliding gulls who respond immediately and autonomously to unknown changes in flow to maintain stability and control in unpredictable environments. We strive to translate this knowledge to flight control of UAVs. Last, a way forward is suggested to create new class of structures: autonomous multifunctional structures. An outline of what is needed in terms of future research is presented.
The flight differences between quadrotors and winged UAVs are the distinctions between agility, maneuverability and endurance. Multirotor UAVs can fly through dense forests, urban canyons and even into and out of buildings, but they lack endurance limiting them to short duration flights. Winged UAVs have larger endurance but are not able to negotiate dense areas. Avian species handle both tasks by their incredible ability to morph into a variety of shapes depending on the demands of the current environment. The research presented here examines how winged UAVs could be morphed to accommodate a variety of tasks through morphing by investigating the mechanics and aerodynamics of morphing UAVs. A great deal of attention over the last few decades has focused on various types of morphing across various scales and applications. One area that remains underdeveloped is that of large motion morphing with articulated wings and tails with the goal of providing the ability to switch between highly agile/maneuverable configurations and long endurance.